The potential importance of artificial intelligence application in breast cancer screening

Abstract

Breast cancer represents a leading public health problem due to its high incidence, morbidity, and mortality. Significant geographic differences in disease burden are influenced not only by socioeconomic and behavioural factors but also by the resources of health systems, access to healthcare, and the quality of diagnostic programs. Modern mammography screening programs substantially contribute to early disease detection, while existing challenges include limited resources, restricted access and inequalities in healthcare, high costs, variability in result interpretation, and the need for additional and invasive diagnostic procedures. To address these challenges, artificial intelligence systems have been developed, employing complex algorithms, machine learning, and deep learning for the automated analysis of mammographic images. These systems can improve diagnostic accuracy, reduce reading time, lower false-negative rates, and decrease the need for repeat diagnostic procedures. Research has demonstrated that the accuracy achieved through artificial intelligence algorithms is comparable to that of radiologists, and optimal results may be obtained by combining radiologist's assessments with artificial intelligence methods, particularly in the role of the first reader. Key limitations of these methods include insufficient adaptability to diverse populations and the risk of over-reliance on algorithm results by less experienced radiologists. Beyond technical challenges, an important public health aspect concerns women's attitudes toward the use of artificial intelligence, as the level of trust in this technology may affect screening participation rates. The integration of artificial intelligence into screening programs requires careful evaluation of benefits and limitations, transparency in technology use, and assurance of human oversight. Proper implementation and public education can contribute to improved early diagnosis, greater screening accuracy, and more efficient use of health system resources.